{"id":11725,"date":"2026-09-13T22:41:46","date_gmt":"2026-09-13T14:41:46","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=11725"},"modified":"2026-09-13T23:15:44","modified_gmt":"2026-09-13T15:15:44","slug":"jase-202612-35-034","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202612-35-034","title":{"rendered":"Cross Modal Fusion Network: Integrating Visual Language Features for Context Aware Visual Communication Design"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 35<\/a><\/p>\n<\/div>\n\n\n\n

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Ying Zhang<\/i><\/a><\/p>\n\n\n\n

Xinxiang Vocational and Technical College Henan, 453000, China<\/p>\n<\/div>\n\n\n\n

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Received: May 09, 2026
Accepted: August 05, 2026
Publication Date: September 13, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Overall architecture of the proposed model <\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation: BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202612_35.034<\/a> <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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Multimodal information integration is essential for developing context-aware visual communication designs, especially as digital media increasingly relies on flexible and efficient interaction. Traditional approaches often treat text and images as separate elements, limiting contextual coherence and weakening message clarity. This study proposes a cross-modal fusion network that integrates visual and linguistic information to enhance context-aware visual communication. Using the Scene Parse 150 dataset, preprocessing involved tokenization, image resizing, and feature extraction through a convolutional neural network. The model introduces a Self-Attention Based Generative Adversarial-tuned Robustly Optimized BERT Pretraining Approach (SAGA-ROBERTa), where RoBERTa encodes textual descriptions to capture semantic richness and guide a generative adversarial network in producing or refining visual design layouts. A multi-channel fusion module with self-attention further captures intrinsic relationships between modalities, ensuring strong semantic alignment. The discriminator evaluates visual realism and coherence with textual intent. Experimental results demonstrate that SAGA-ROBERTa achieves notable improvements, including an Average IoU of 68.6% and pixel accuracy of 94.5%, outperforming conventional unimodal methods. The findings highlight the potential of cross-modal deep learning frameworks to support more adaptive, emotionally resonant, and semantically precise visual communication across diverse media contexts.<\/p>\n\n\n\n

Keywords: Cross-Modal Fusion, Visual-Language Integration, Context-Aware Design, Visual Communication Design, Self-Attention Based Generative Adversarial-tuned Robustly Optimized BERT Pretraining Approach (SAGA-ROBERTa).<\/em><\/p>\n\n\n\n

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